基于指数增益迭代学习算法的机械臂轨迹跟踪  

Robotic Arm Trajectory Tracking Based on Exponential Gain Iterative Learning Algorithm

作　　者：郑铭跃 张方[1] 蒋祺[1] 蒙奎全 朱伟 ZHENG Mingyue;ZHANG Fang;JIANG Qi;MENG Kuiquan;ZHU Wei(State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics&Astronautics,Nanjing Jiangsu 210016,China;Kunshan Huaheng Welding Co.,Ltd.,Kunshan Jiangsu 215300,China)

机构地区：[1]南京航空航天大学机械结构力学及控制国家重点实验室,江苏南京210016 [2]昆山华恒焊接股份有限公司,江苏昆山215300

出　　处：《机床与液压》2022年第15期15-19,共5页Machine Tool & Hydraulics

摘　　要：工业机器人是有着非线性、强耦合以及时变性等特点的复杂动力学系统。针对某公司的六自由度机器人的轨迹跟踪问题,利用拉格朗日法对机器人进行了动力学建模,并提出一种指数增益的迭代学习控制算法以对机器人进行轨迹跟踪,并分析了该方法的收敛性。利用MATLAB软件中的Simulink功能对机器人模型工作轨迹进行仿真,将结果与传统PD型迭代学习算法进行比较,证明了该控制算法的有效性与可用性。Industrial robots are complex dynamic systems with nonlinear,strong coupling and time-varying characteristics.Ai⁃ming at the trajectory tracking problem of a six-degree-of-freedom robot,the Lagrange method was used to build the dynamics model of the robot,and an exponential gain iterative learning control algorithm was proposed to control the robot trajectory track.The conver⁃gence of the method was analyzed.The MATLAB/Simulink was used to simulate the working trajectory of the robot model,and the test results were compared with the traditional PD-type iterative learning control algorithm.It proves the effectiveness and usability of the control algorithm.

关 键 词：机械臂 动力学建模 轨迹跟踪 迭代学习算法 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]